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Journal receives papers in continuous flow and we will consider articles from a wide range of Information Technology disciplines encompassing the most basic research to the most innovative technologies. Please submit your papers electronically to our submission system at http://jatit.org/submit_paper.php in an MSWord, Pdf or compatible format so that they may be evaluated for publication in the upcoming issue. This journal uses a blinded review process; please remember to include all your personal identifiable information in the manuscript before submitting it for review, we will edit the necessary information at our side. Submissions to JATIT should be full research / review papers (properly indicated below main title).

Journal of Theoretical and Applied Information Technology
December 2022 | Vol. 100 No.23

Title:	APPLICATIONS OF ARTIFICIAL INTELLIGENCE METHODS FOR ENHANCING INFORMATION SHARING IN SUPPLY CHAINS: SYSTEMATIC REVIEW
Author:	NISRINE ZOUGAGH, ABDELKABIR CHARKAOUI, YASSINE ZOUITA
Abstract:	Supply chain Management SCM, improving Information Sharing IS becomes increasingly important to promote business, achieve a significant competitive advantage, and, ultimately, assure the survival and growth of firms. This paper reviews how artificial intelligence AI methods can improve SI in SCM by performing a systematic literature review. Its goal is to find out current AI techniques that can improve IS in SCM. According to our findings, Demand forecasts are the main shared information that attracted more attention. In addition, we found that AI methods are most commonly applied in production management. Furthermore, Machine learning (ML) is the most widely employed AI subset for enhancing IS in SC and the artificial neural network (ANN) is the most popular ML method.
Keywords:	Artificial intelligence, Information sharing, supply chain management, Artificial neural network, PRISMA
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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Title:	CLOUDIOT-BASED HEALTHCARE ADOPTION BY HEALTHCARE PROFESSIONALS: A CONCEPTUAL MODEL
Author:	IYAD ALTAWAIHA, RODZIAH ATAN, RAZALI BIN YAAKOB, RUSLI BIN HJ ABDULLAH, RADHWAN SNEESL
Abstract:	In hospitals and healthcare facilities, the conventional healthcare setting is highly monotonous and inefficient, and it does not scale up to meet the present demand for healthcare services. Driven by the increased world population aging and the spread of pandemics, it has become essential to build cohesive and organized healthcare systems that seek to reduce clinical costs and the burden placed on healthcare institutions. The fast development of new technologies has recently sparked a worldwide revolution dubbed the 4th Industrial Revolution that used to improve healthcare services and introduce the concept of Healthcare 4. An example is CloudIoT-based healthcare which contributes to the development of effective healthcare systems that manage and monitor hospitals and patients and improve the quality of healthcare services. Although there are many benefits to using this technology, there is a disparity between its advanced development and its actual usage among healthcare professionals. This research study determines a gap in the current research on investigating the factors influencing healthcare professionals' adoption of this technology since prior studies focused primarily on the functionality and design of its systems, while adoption of such systems is lacking in these studies. The most common models for predicting and evaluating technology acceptance and use are the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). The aim of this study is to review the relevant literature for these three models and provide a conceptual model for healthcare professionals' adoption of CloudIoT-based healthcare.
Keywords:	Healthcare systems; 4th Industrial revolution; Healthcare ; CloudIoT-based healthcare; TAM; UTAUT; UTAUT2; Healthcare professionals' adoption.
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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Title:	ONTOLOGY BASED KNOWLEDGE MODELLING FOR INDONESIAN RICE VARIETIES
Author:	SOFI DEFIYANTI, AHMAD ASHARI, DANANG LELONO
Abstract:	Knowledge is an asset for every organization, including knowledge about new superior varieties of rice developed by the Agricultural Research and Development Agency of the Ministry of Agriculture of the Republic of Indonesia. Until 2021, as many as 120 new superior varieties of rice have been published, but knowledge of the improved varieties developed is only limited. Many varieties cause farmers to be confused in determining the rice varieties to be planted. So that a knowledge model is needed that can formalize knowledge about rice varieties, one way is to develop a rice variety ontology model. We propose the Ontology Varieties Rice (OntVarRice), an ontology of new superior rice varieties in Indonesia, by following the MethOntology methodology, which consists of a specification, conceptualization, formalization, and implementation of the ontology. OntVarRice includes 32 classes, 16 object properties, 7 data properties, 167 individuals, and 2430 logical axioms implemented using OWL. Evaluation and validation using HermiT to test consistency and coherence, and Description Logic query (DL query) is used to verify the knowledge built based on the answers to competency questions. OntVarRice as a knowledge-bases model can improve agricultural practices to make optimal decisions because OntVarRice stores and models knowledge about new superior rice varieties in the form of a complete picture of the concept of new superior rice varieties that can maximize variety selection, yield potential, production quality, pest, and disease resistance.
Keywords:	Ontology Deployment, Varieties, Rice, Knowledge-Based, Agriculture
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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Title:	EFFICIENT EMPIRICAL ENVIRONMENTAL FORECASTING USING INTERNET OF THINGS AND MACHINE LEARNING ASPECTS
Author:	Dr T Preethi Rangamani, Dr R Srinivasa Rao, Dr P Sumalatha, Dr L Bhagya Lakshmi, Dr Shaheda Niloufer, Dr V Parvathi, Kodepogu Koteswara Rao, Mr. P Anil Kumar
Abstract:	The Internet of Things (IoT) gives a virtual view, via the Internet Protocol, to a huge variety of real life objects, ranging from a car, to a teacup, weather atmosphere etc. The Internet of Things (IoT) is the network of physical objects, devices, vehicles, buildings and other items which are embedded with electronics, software, sensors, and network connectivity, which enables these articles to gather and trade information. WSNs are integrated into the Internet of Things (IoT), where sensor nodes join the Internet dynamically, and use it to collaborate and accomplish their tasks. Wireless sensor networks (WSN) are well suited for long term environmental data acquisition for IoT representation. And we also discuss More and more natural disasters are happening every year: floods, earthquakes, volcanic eruptions, etc. So as to decrease the risk of possible damages, governments all around the world are investing into development of Early Warning Systems (EWS) for environmental applications. The most essential assignment of the EWS is identification of the onset of critical situations affecting environment and population, early enough to inform the authorities and general public. This paper portrays a methodology for observing of surge securities systems based on machine learning methods.
Keywords:	Environment, IoT, Forecasting, Machine Learning Aspects, Effecient
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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Title:	AN ENERGY EFFICIENT APPROACH FOR BIG DATA MASS STORAGE SYSTEMS USING A SEQUENTIAL CACHE
Author:	AHMED F. MHDAWY, MAEN M. AL ASSAF
Abstract:	Big data storage centers became important in the recent era due to the advances in today’s world cloud systems and the increased amount of the data generated by users. Mass-inexpensive storage devices are still used in order to provide storage for a lower cost. It is a fact that data retrieval from commonly used mass-inexpensive storage devices (i.e. HDDs and Tapes) consumes much power due to their cost-inefficient hardware architecture (i.e. arm moves over different tracks on magnetic disk) especially with randomly stored data blocks. Retrieving sequentially stored data blocks tends to consume less power as they require less arm moves. Hence, the need for energy efficient solutions is important. In this research, we present an energy efficient solution for big data storage system using sequential caching approach. It is practically proven that users’ I/O requests for data tend to show repeating patterns. Data caching that keeps the most frequently accessed data blocks showed efficient performance improvement for many solutions over the last decades. Our Solution (EEHSC) boosts data caching and arm moves’ space locality by buffering most frequently used data blocks in the middle of the storage medium. This in result reduces power consumption by increasing the percentage of data blocks that are read sequentially over those that are read randomly. We run performance evaluation for our EEHSC algorithm using a simulator created by ourselves based on real world I/O traces. Results show an EEHSC can efficiently reduce power consumption without the need for allocating big size cache due to the high data hit-rate that can be achieved in the cache. Results showed that power saving ratio can reach 67.60% when using LASR machine01 trace and 36.74% when using LASR machine06 trace. This difference goes to the variation of the repeated pattern for the most frequently used data blocks.
Keywords:	Energy Efficiency, Big Data Centers, Caching, Hard Desk Drive, Magnetic Storage
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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Title:	IMAGE RETRIEVAL USING COLOR AND STRETCHED TEXTONS OF RULE BASED MOTIF
Author:	M. VIJAYASHANTHI, DR.V.VENKATA KRISHNA, DR. G. VENKATA RAMI REDDY
Abstract:	By deriving color and texture attributes, this paper develops a new framework for content-based image retrieval (CBIR). The individual histograms on the H, S, and V planes are calculated in the first step. This paper primarily divided the 3x3 window into four overlapped microgrids of size 2x2. On each micro grid, it has derived Rule-based Dynamic motif (RM) indexes. The RM extracts all probable Motifs on a 2x2 grid by overcoming all possible ambiguities. This process transforms the 3x3 window into a 2x2 grid and each grid value represents the shape, and texture features more efficiently and precisely. On this transformed grid of 2x2, this paper applied Stretched Texton (ST) patterns. The ST patterns consider the magnitude relationship among the pixels that are part and not part of texton formation. This aspect is not considered in the earlier methods. The co-occurrence features are derived from RM-ST matrix (RMSTCM). The feature vector is derived by fusing features of RMSTCM with the histogram features of color components of H, S, and V. The suggested framework is related with the other methods of significance. The outcomes specify the efficacy of the RMSTCM.
Keywords:	Histograms; Texture; Stretched Textron's; Motifs; Co-Occurrence Feature .
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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Title:	MANIPULATION PREVENTION IN GOLD AND MINERAL PARTICLES PRODUCTION USING BLOCKCHAIN AND SMART CONTRACTS
Author:	MAHMOUD ABD ELNABY A. HEGAZY, SHEREEN A. TAIE, SHEREEN A. HUSSIEN
Abstract:	Blockchain technology has an effective role in multi-step transactions. Blockchain technology availability of new solutions and innovations has made a real change in busines. Smart contracts are computer protocols designed to facilitate and verify the agreements among multiple users automatically when certain conditions are met. With the rapid advance in blockchain technology, smart contracts are being used to serve a wide range of purposes ranging from self-managed identities on public blockchains to automating business collaboration on permissioned blockchains. They contribute in the development of many decentralized applications for all domains. The gold particles and precious metals production from rocks is one of the fields that can be improved by using Blockchains. This paper provides a model based on the private blockchain permissioned system in recording and monitoring the results of the mineral exploration process in rocks and the result of the process data analysis of atomic absorption to prevent any manipulations. It is implemented with Hyperledger fabric platform based on consensus Federated Byzantine Agreement (FBA). It achieves a remarkable level of scalability, transparency and safety.
Keywords:	Manipulation Prevention, Previous Metals, Blockchain, Smart Controls
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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Title:	A COMBINED FUZZY MULTI-CRITERIA DECISION MAKING APPROACH FOR GREEN SUPPLIER SELECTION IN BUILDING MATERIAL INDUSTRY
Author:	EL BETTIOUI WISSAL, ZAIM MOUNIA, SBIHI MOHAMED
Abstract:	With the growing awareness of environmental protection and increasingly demanding customers, the integration of green practices into supply chain management has become an important issue for companies in different industry sectors. It is now essential for managers to review their strategies and improve the performance of their decision-making systems if they want to maintain their competitiveness. This paper proposes a green supplier selection and evaluation model for material building sector that takes into account both traditional and ecological characteristics. This hybrid model incorporates two well-known decision-making approaches, Fuzzy Analytic Hierarchical Process (Fuzzy AHP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS). The selection criteria are determined on the basis of a detailed analysis of the existing literature and a series of interviews with the expert team members, taking into account the characteristics of the studied sector. These criteria are evaluated and weighted by the Fuzzy AHP method and subsequently, based on the Fuzzy AHP weights, Fuzzy TOPSIS is applied to rank the potential suppliers. In order to prove the efficiency and the applicability of the suggested approach a real-world case study is conducted to evaluate three green suppliers of a Moroccan ceramic tile company. And finally a sensitivity study is performed to assess the impact of criterion weights on the supplier ranking order.
Keywords:	Green supplier selection, Multi criteria decision-making, Fuzzy set theory, Fuzzy AHP, Fuzzy TOPSI
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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Title:	IS COMPUTATIONAL ALGORITHM FOR HIGH MINING ITEMSETS EFFECTIVE?
Author:	SIVA S, SHILPA CHAUDHARI, S. PRAVEEN KUMAR
Abstract:	Every day the number of datasets is increasing, and the advancement also leads to different research algorithms to extract meaningful information about personal interests. The problem of frequent itemsets from analysing different periodical itemsets from the large chunks of data has always been a keen research area for data analysts. Therefore, the most valuable fields of data mining are computational high-utility itemset mining i.e., used to obtained valuable knowledge from a homogeneous mixture of data. In the past years, various algorithms and works are proposed by different researchers to solve the issues of high-utility or frequent itemsets mining in transactional databases; however, an essential limitation of the algorithms is static nature [14]. In real-life applications, datasets are often dynamic and change according to different parameters such as price, weight, quality etc. Therefore, a transformation of the existing database and redefinition of parameters is required for gaining maximum profits from the high-utility itemsets mining. In this research, a novel approach is designed and developed i.e., a computational intelligence approach based on high-utility itemset mining algorithm for efficient analysis of periodical itemsets from the transactional database [19]. The proposed MFHUIM algorithm is further experimented on retail BMS database using Java-based environment and results of both the algorithms based on runtime, memory usage and scalability are detailed in the research work. MFHUIM outperforms EFIM algorithm in extraction of the accurate high utility itemsets in a large set of databases.
Keywords:	Periodical Itemsets, High-Utility Itemsets, Transactional Database, Homogeneous Dataset, Pattern Mapping
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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Title:	SUCCESS FACTORS ON THE IMPLEMENTATION OF HUMAN-RIGHTS-AWARE CITIES SCORING APPLICATION
Author:	JAROT S. SUROSO, GINANJAR MULYO UTOMO
Abstract:	As an effort to advance human rights in every city across Indonesia, the regional government of all cities should fulfill basic human rights need in their cities to advance Human Rights in Indonesia even further. The Human-Rights Cities Scoring program brought by Ministry of Law and Human Rights of Republic of Indonesia via its Directorate General of Human Rights helps them to achieve it. To make sure the scoring run smoothly and to accommodate a new Ministerial Decree with an increasing number of indicator criteria, a web-based application is used since 2017. However, since the number of cities getting the title went stagnated since the application is used, this research wants to see what factors affecting the success of the implementation of this application. After asking 182 respondents and analyzing the data using the PLS-SEM method, this research found that information quality, system quality, and service quality are not significantly affecting both intentions to use and user satisfaction. In the other hand, the influence of social influence, intention to use, use, and user satisfaction are all significantly positive.
Keywords:	Information Systems, e-Government, DeLone and McLean IS Success Model, PLS-SEM, Human Rights
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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Title:	ESTIMATION OF PATH ANALYSIS WITH JACKKNIFE AND BLINDFOLD RESAMPLING APPROACH
Author:	ATIEK IRIANY, HENIDA RATNA AYU PUTRI, ARSID JIMMI YUWANTO
Abstract:	Because it offers a variety of simple information that customers can obtain only online, websites are frequently used by criminal enterprises to conduct business. Marketplace is a sort of website that is currently widespread and used to transact business. between buyers and sellers. Increase the number of people that visit marketplace websites by promoting them on Instagram. For more information on these impacting elements, check out the Instagram marketplace and the websites that host online engagement feeds. An example of a marketplace was obtained from the website iPrice, which works with several marketplaces. This study's method analysis is a track of analysis used to determine how exogenous and endogenous variables interact. Because this research's testing of the hypothesis was done with a blindfold and a jackknife, the test findings demonstrate that the variable online engagement and visits marketplace have error that is not regularly distributed. Based on relative computation efficiency, the jackknife method yields better results than the tested blindfold method, which has more minor scoring alternatives. 96.7 percent of the diversity of data can be explained by a model study, while the remaining 3.3 percent is explained by other variables that are not included in models. A comparison of two resampling methods, namely jacknife and blindfold, was carried out to obtain an effective path coefficient value in researching Instagram marketplace uploads on marketplace website visits.
Keywords:	Path Analysis, Blindfold, Jackknife, Marketplace, Online Engagement, Uploads Instagram
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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Title:	EVALUATING USER ADOPTION ON SALESPERSON MOBILE APPS BY USING TTF AND UTAUT
Author:	RYO SEGARA, AHMAD NURUL FAJAR
Abstract:	The banking industry is one of the industries affected by the Covid-19 pandemic. The reason is that as a provider of financial services, the Bank will be very dependent on customer growth. This growth is achieved by establishing an intensive direct relationship with its customers, which is a challenge during the pandemic. One of the largest national private banks in Indonesia was developed mobile application for supporting their sales’ daily work, named OS. With OS application sales can manage their pipeline digitally, get promotion info easily, do opening account independently, and get customer’s information on their hand. However, the level of user adoption is still very low, this is a concern of management. This study is intended to see what factors influence the level of user adoption of using OS applications. By using two frameworks, UTAUT and TTF, we want to see the level of user adoption, which is more comprehensive regarding the use of OS applications by sales at Bank XYZ. The results show that there is a correlation between TTF and UTAUT structures. Technology characteristics have a large impact on effort expectations, and task technology suitability has a clear impact on performance expectations.
Keywords:	UTAUT, TTF, Mobile Apps, User Adoption, Sales
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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Title:	RESEARCH TRENDS AND METHODS FOR RECOMMENDATION SYSTEM IN EDUCATION: A REVIEW
Author:	HENDRA MAYATOPANI, NOR ADNAN YAHAYA, DINA FITRIA MURAD
Abstract:	This paper presents a review on the research trends and methods used in recommendation systems for the education domain over the last five years (2017 – 2022). The sources of literature were taken from 8 digital libraries where 29 papers were then selected for review using the PRISMA technique. This systematic literature review finds that the most widely used method is collaborative filtering (53%), followed by content-based (29%). It also reveals that researches in recommendation systems for education are still being carried out in 14 countries. These results show that there are still many opportunities for informed development and utilization of recommendation systems in learning engagement within virtual environment.
Keywords:	Recommendation System(RS), Education, Trends
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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Title:	DEEP LEARNING-XCEPTION ALGORITHM FOR UPPER BONE ABNORMALITIES CLASSIFICATION
Author:	ALAA M. A. BARHOOM, MOHAMMED RASHEED J. AL-HIEALY, SAMY S. ABU-NASER
Abstract:	Upper bones are strong and flexible tissue made up of collagen and calcium phosphate. They mainly contribute to the movement of the human body and serve as a protective shield for the body's soft organs such as the brain, lungs, and the heart. Without these bones, the human body would not be constructed to function ordinarily. However occasionally, due to accidents, an individual is exposed to some diseases such as injury or infection that lead to defects in the regular shape and growth of bone construction. This deficiency in the bone structure is so-called bone abnormalities. Frequently, the preliminary diagnosis of bone abnormalities is made by specialists using X-rays of the patient's injury site to show the shape and density of the bones. They are classified into normal or abnormal. The detection and classification of bones depend on the experience and human effort. So the error in the results of this process can expose the patient to a great danger and catastrophe of his life. Therefore, deep learning algorithms from artificial intelligence were applied to help specialists avoid wrong or inaccurate diagnoses when detecting bone abnormalities in X-ray images by using a pre-trained convolutional neural network called Xception model. The model was customized to fit the bone abnormalities classification then applied to a dataset consisting of 42000 X-rays of the upper bones of some patients collected from Kaggle depository. We trained, validated, and tested the customized Xception model. The proposed Xception model attained Precision (85.20%), Recall (85.13%) and F1-Score (85.07%).
Keywords:	Bone Abnormalities, Deep Learning, Xception
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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Title:	IMAGE ENCRYPTION METHODOLOGY BASED ON CELLULAR AUTOMATA
Author:	NASHAT AL BDOUR
Abstract:	The paper considers a methodology for encrypting color images, in which the key is the initial state of an elementary cellular automata for the implementation of evolution based on a given rule. The research task is to improve the reliability of encryption of color images based on the encryption of the bit layers that make up the image. To solve this problem, the methodology of forming the evolution of an elementary cellular automata was used, which is a finite bit key array for each bit layer of the image. The encryption and decryption key consists of subkeys, the number of which corresponds to the number of bits that encode the color of each pixel. Each subkey consists of the initial states of an elementary cellular automata and the rules that shape its evolution. For the formation of each bit key array, different initial conditions and different Wolfram rules for elementary cellular automata were used. The size of each formed key bit array is equal to the size of the corresponding bit layer of the color image. Encryption is performed by using the XOR function for the generated key bit array and bit-slice of the image. As a result of the experiments, it was established that it is necessary to use different rules that form different geometric shapes in evolution. It is also established that it is necessary to form a key bit array for each bit layer starting not from the first lines of evolution. It was found that the quality of encryption of a color image is most influenced by the three most significant bits of each byte of the pixel code, encoding the corresponding red, blue and green colors.
Keywords:	Encryption, Image, Cellular Automata, Evolution, Wolfram's Rule, Key Bit Array, Bit Layer.
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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Title:	PREDICTION OF THE MOST EFFECTIVE ADJUVANT THERAPEUTIC COMBINATIONS FOR BREAST CANCER PATIENTS USING MULTINOMIAL CLASSIFICATION
Author:	MEROUANE ERTEL, AZEDDINE SADQUI, SAID AMALI, NOUR-EDDINE EL FADDOULI
Abstract:	The main goal of precision medicine in the fight against cancer is to predict effective treatment modalities based on the unique molecular genetic profiles of a tumor. Understanding the factors that influence treatment success is critical because people with breast cancer at similar stages respond differently to treatment. In order to reduce the likelihood of recurrence of metastases in breast cancer patients, this study proposes a supervised multinomial logistic regression model. This model will help clinicians make decisions about which treatment plans they should recommend to patients. In addition, this article compares a number of polynomial machine learning technologies, including Naive Bayes, Decision Tree, Support Vector Machine, Random Forest, and Neural Network (ANN). Accuracy results for adjuvant treatment combination prediction show that the Random Forest classifier is more accurate.
Keywords:	Machine Learning; Multinomial Logistic Regression; Personalized Medicine; Multi-Class Classification; Adjuvant Therapy; Breast Cancer.
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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Title:	ANALYSIS OF FACTORS CAUSING INFORMATION SYSTEMS PROJECTS DELAYS IN IT CONSULTING COMPANY
Author:	HELMI YOGAANTARA , AHMAD NURUL FAJAR
Abstract:	As a banking IT consulting firm, the company carries out many projects. Every project is always prepared as well as possible, however not all projects go according to plan, and the issue of delay has received considerable critical attention. Due to delays the company suffered a loss of time and costs, project delays are also declared as failed project categories. This study aims to analyze the effect of poor requirements management, complexity, and employee issues on the occurrence of delays in IS/IT projects in the company. Data collection was carried out by distributing questionnaires to 138 employees of the company. The study used a quantitative approach with the SEM-PLS method and the data was processed using the SMART PLS v.3.3.3 application. The results of the hypothesis test show that poor requirements management and employee issues have a positive effect on project delays, while complexity does not have a positive effect on project delays. The results of this study are expected to be the basis for evaluating the company for better project sustainability.
Keywords:	Information System, Project Management, Project Delay, Project Failure, SEM-PLS.
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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Title:	SECURE AUTHENTICATION SCHEME BASED ON NUMERICAL SERIES CRYPTOGRAPHY FOR INTERNET OF THINGS
Author:	MAHA ALADDIN, KHALED NAGATY, ABEER HAMDY
Abstract:	The rapid advancement of cellular networks and wireless networks has laid a solid basis for the Internet of Things. IoT has evolved into a unique standard that allows diverse physical devices to collaborate with one another. A service provider gives a variety of services that may be accessed via smart apps anywhere, at any time, and from any location over the Internet. Because of the public environment of mobile communication and the Internet, these services are highly vulnerable to a several malicious attacks, such as unauthorized disclosure by hostile attackers. As a result, the best option for overcoming these vulnerabilities is a strong authentication method. In this paper, a lightweight authentication scheme that is based on numerical series cryptography is proposed for the IoT environments. It allows mutual authentication between IoT devices. Parametric study and formal proofs are utilized to illustrate that the proposed approach is resistant to a variety of security threats.
Keywords:	Internet of Things, Confidentiality, Authentication, Cryptography, Security Scheme, BAN Logic
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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Title:	PACKET LENGTH COVERT CHANNEL DETECTION: AN ENSEMBLE MACHINE LEARNING APPROACH
Author:	Muawia. A. Elsadig , Ahmed Gafar
Abstract:	The use of covert channel techniques has increased the capacity to carry out dangerous and undetectable attacks. Traditional security procedures cannot identify them because they utilize methods not meant to transmit information. A covert channel type that is difficult to identify, reduce the impact of, or eradicate is a packet-length covert channel. This covert channel makes use of differences in network packet lengths to send secret messages. Recent studies have emphasized the advantages of using machine learning techniques to identify covert channel attacks. As a result, this work offered an effective ensemble classification model to find these kinds of assaults. Three machine learning techniques make up the ensemble model, which serves as our model's primary classifiers. These classifiers consist of Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB) (SVM). The output of the proposed ensemble classifier was produced by combining the primary classifiers' outputs using the logistic regression (LR) classifier which is served as a meta classifier. Our proposed ensemble model performed well, according to the results. It surpasses all single classification algorithms by achieving a considerable accuracy rate to detect such type of covert channel attacks.
Keywords:	Covert Channels, Packet Length Covert Channels, Network Attacks, Machine Learning, Ensemble Classification, Deep Learning, Stacking Technique.
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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Title:	A NEW WECHAT MOBILE INSTANT MESSAGING SOFTWARE: BASED ON THE THEORY OF USE AND SATISFACTION AND THE THEORY OF PLANNED BEHAVIOR
Author:	HUAXIANG LIU, KUOK TIUNG LEE
Abstract:	With over one billion monthly active users, Chinese social networking and multipurpose software Wechat has become one of the world's most popular social media platforms. Wechat has gradually risen to the top of the social media heap among Chinese teenagers. Because of this, little is known about how people's usage of Wechat is influenced by psychological factors. In order to build an integrated model that can predict and explain a person's ongoing use of Wechat, researchers utilized the theory of use and satisfaction (TUS) and the theory of planned behavior (TPB). Researchers used an upgraded version of the TPB model that incorporated the extra variables of self-identity and belongingness to predict long-term Wechat usage intentions and behavior in a sample of Chinese adolescents. Further studies looked at the impact of Chinese adolescents' sense of self-identity and belongingness on their usage of Wechat. regression studies partly confirmed the TPB: attitude and subjective norm substantially predicted intends to continue using Wechat, and intention significantly predicted behavior. Intention and, perhaps surprisingly, behavior were strongly predicted by self-identity, but not by belongingness. Prior behavior also had a strong influence on both intention and behavior. Wechat addiction was shown to be strongly correlated with feelings of self-identity and belongingness. Fuzzy based theories of usage and satisfaction and hidden markov theories of planned behavior are also used in this research to investigate why adolescents use WeChat on their mobile phones. Consumer involvement is examined for its effect on motivating demands and as a mediating factor in problematic usage. All hypotheses are tested using techniques such as factor analysis, correlation analysis, and structural equation modelling, which are based on Wechat user surveys. These results may guide efforts aimed at modifying Chinese teens' ongoing usage of Wechat or addictive tendencies for Wechat.
Keywords:	Wechat, Chinese teenagers, Fuzzy based Theory of use and satisfaction, hidden markov based Theory of planned behavior (TPB)
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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Title:	AN IMPROVED LOUVAIN ALGORITHM BASED ON NODE IMPORTANCE FOR COMMUNITY DETECTION
Author:	ARWA ALDABOBI, AHMAD SHARIEH, RIAD JABRI
Abstract:	Many algorithms have been developed to solve the problem of detecting and analyzing community structure in networks. Louvain algorithm (LVA) is a well-known community detection method that results high community structure of large networks within reasonable time, but it has the problems of randomness and instability. In this paper, an improved Louvain algorithm (ILVA) is proposed by combining the modularity function and node importance with the original LVA. The ILVA uses the LVA to detect community structure by optimizing the value of modularity. Meanwhile, node importance as measured by degree centrality is used to determine the node scanning order in the community detection phase. Experiments were conducted on real-world networks and the results showed that the ILVA produced stable community structure with higher modularity within reasonable time.
Keywords:	Community Detection, Randomness, Louvain, Modularity, Node Importance
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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Title:	ANALYSIS AND DEVELOPMENT OF MICROSERVICES ARCHITECTURE IN LOAN APPLICATION SYSTEM OF COOPERATIVE ENTERPRISE IN INDONESIA
Author:	REYNALDI LIE, AHMAD NURUL FAJAR
Abstract:	PT XYZ is a cooperation enterprise currently using monolithic IT (Information Technology) system architecture. Load test and stress test results of the system showed that development of capabilities of the loan application system of the enteprise is still possible to be done. This research is purposed to analyze the loan application system design based on microservices as possible alternative to replace the current IT monolithic system architecture in PT XYZ by enhancing the performance of the loan application system in scaling up business with the ability to establish low dependency among applications. The research is exclusively aimed to solve the system architectural problem in cooperative enterprises with PT XYZ as the example. The method used in this research is microservices-based system design with DDD (Domain Driven Design) approach by determining bounded contexts, followed by classification of entities, aggregates, and services that are going to be materialized in the design. Analysis results confirmed that the services can be seen from three different contexts, namely information context, loan application context, and loan review context according to the functionalities of each service component. The researcher suggested software stack emphasizing on processes automation at PT XYZ to support the microservices architecture design.
Keywords:	Service Oriented Architecture, Microservices Design, Domain Driven Design, System Architecture, Loan Application
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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Title:	A FRAMEWORK FOR SINGLE-CHIP MULTIPLE PROCESS MICROARCHITECTURE FOR DYNAMIC IOT COMMUNICATION
Author:	NITESH GAIKWAD, Dr. SHIYAMALA. S
Abstract:	The presence of high-performance multicore embedded architectures, energy efficiency remains a research black hole. Previous algorithms like Dynamic-based Frequency Scaling models and mapping-based thread models were implemented in microarchitecture to efficiently use clock frequency and energy. Unfortunately, these methods raise the problem of data traffic and high execution time. To overcome these drawbacks, a solution model analyzed load in the multiprocessor and migrated them to the proper Core to give an efficient data transfer rate. To perform this, an African Buffalo Load Migration Communication (ABLMC) system model has been presented in this paper. This unit analyses the workload characteristics of the multicore processor in the dynamic IoT environment application layer. Workload parameters are taken as features and computed for their similarity with minimum execution time using optimization. The predicted score value has been then given to the ABLMC model to take migrating decisions on loads. Communication requires a high data transfer rate that was achieved in a multicore processor using this ABLMC model. The proposed framework has been implemented and tested in a MATLAB environment with the performance matrices of high Data transfer rate and minimum execution time of 1.6 s. Thus, the proposed framework has excellence in real-time applications.
Keywords:	Microarchitecture, Multiprocessor, Core, Optimization, Iot Communication, And Workload Characteristics.
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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Title:	HEART DISEASE PREDICTION BY STACKING ENSEMBLE MODELS ON MULTIPLE CLASSIFIERS BY APPLYING FEATURE SELECTION METHODS
Author:	GYANENDRA KUMAR PAL, SANJEEV GANGWAR
Abstract:	Cardiovascular disease is regarded as one of the main sources of death for the entire planet. Drawing the conclusion of cardiovascular failure is a difficult task, especially in immature and agricultural countries that lack human experts and types of equipment. Since then, various experts have created various intelligent frameworks to mechanize the identification of cardiovascular diseases. Feature importance/selection is a key part of the medical data set. In this article, we propose a diagnostic system that uses chi2, logistic regression (LR), Pearson, recursive feature elimination, and random forest (RF) as feature selection and several classifiers to predict heart disease. Among the 11 features in the heart disease data set, important features were selected. Apply accuracy and other measures such as precision, sensitivity, specificity, F1 score, ROC (receiver operating characteristic), Log_Loss, mathew_corrcoef, and confusion matrix to compare the data set with all features and selected features. Experimental results show its effectiveness and effectiveness in predicting heart disease. In addition, the proposed model shows better performance compared to the previously proposed model. In addition, our proposed method achieves a high prediction accuracy of 82.95%. Our results show that the proposed method can be reliably used to predict clinical heart disease.
Keywords:	Cardiovascular disease, Feature selection, Medical data, Chi2, Logistic regression, F1 score, Confusion matrix.
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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Title:	ASSESSEMNT OF CURRENT METHODS OF CLASSROOM TEACHING BASED ON STUDENTS LEARNING SATISFACTION FOR UNDERGRADUATE ENGINEEIRNG PROGRAM AT AN INDIAN UNIVERSITY
Author:	PALLAVI ASTHANA, ANIL KUMAR, SUDEEP TANWAR,SUMITA MISHRA
Abstract:	Engineering students need to be prepared with cutting-edge knowledge and skills to align with rapid strides in engineering, and technology. This work examined the effectiveness of current teaching pedagogy for classroom teaching through a survey conducted on undergraduate students of Engineering. Analysis of this survey reflected the need of alternate teaching pedagogies based on students’ response. Exploratory Factor Analysis (EFA) confirmed that 75.8% of participating students are into the Multiplicity stage of Perry’s Intellectual growth. These students are able to take charge of their learning needs, hence, can be trusted with their opinions, and they seek a change in the traditional methods of teaching for better course content delivery. For such students, experiential learning-based teaching pedagogy would be highly useful as it emphasizes cognitive development, hence, students are able to understand the topics efficiently. The advantages of employing Experiential based teaching pedagogies have also been discussed in this paper.
Keywords:	Learner’s Satisfaction, Experiential Learning, Perry’s Intellectual Model, Engineering Education, Teaching pedagogy
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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Title:	PERFORMANCE ANALYSIS AND EVALUATION OF IMAGE CLASSIFICATION MODELS USING MACHINE LEARNING
Author:	MOHAMED NOUR, RASHA M. AL-MAKHLASAWY, MAYADA KHAIRY
Abstract:	This work presents an image classification process using machine and deep learning. The machine learning has feature extraction and classification modules. It can extract certain features of images but unable to select differentiating features from the training set of data. Deep learning can find naturally the relevant features for the adopted applications. The convolutional neural network (CNN) is one of the common deep learning approaches. CNN has an input layer, hidden layers, and an output layer. An image is constructed as a matrix of pixels where the pixel values are given to the input layer supported with weights and biases. The hidden layers are convolutional, pooling and/or fully connected layers. The output layer is a fully connected layer to classify the image to which class it belongs to. Moreover, a set of hyper-parameters are analyzed and investigated. The parameters play an important role in the performance of the image classification process. A set of experiments are operated to see the effect of every hyper parameter. The parameters include; but not limited to; the number of hidden layers, the number of epochs, filter size, number of filters, batch size, learning rate, optimization method, and others. Moreover, a useful supervised machine learning approach is adopted to classify the images. The number of selected features has a vital role on the performance of the support vector machine (SVM). Both the CNN and SVM are operated and tested using two big datasets. The first dataset; CIFAR-10; has ten classes and 60,000 images where the second one; MNIST; has ten classes and 70,000 images. The performance of both deep learning and SVM approaches are compared. Some measurable criteria are considered such as accuracy, learning time, prediction time, and others. The classification accuracy using CNN outperforms that accuracy value for the SVM. The performance of CNN using the MNIST dataset is better than the CNN using the CIFAR-10 dataset. This means that the dataset size, nature, and characterization play an important role in the performance of machine and deep learning approaches. The learning time and prediction time for CNN approach are greater than those corresponding values of SVM. The obtained results in this work are better than some of the related efforts published in the literatures by others using the same machine learning approaches and the same datasets.
Keywords:	Image Classification, Machine Learning, Deep Learning, Image Datasets, and Performance Evaluation.
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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Title:	EFSLIM : INFLUENCE MAXIMIZATION USING ENHANCED SHUFFLED FROG LEAPING APPROACH IN SOCIAL NETWORKS
Author:	K. GEETHA, A. R. NASEER, M. DHANALAKSHMI
Abstract:	Social networks are influencing people to make choices and decisions on others. With the advertisement of the business products on the web and other sources, the development of social network has increased tremendously. Many social networking organizations develop their network nodes by using a popular concept known as Influence Maximization, which is a greedy approach. The objective of this approach is to maximize the nodes by identifying minimum subset nodes formed at the base level, which has the capability to influence other nodes. The existing algorithm, Independent Cascade Model, in which the activation probability of every node is computed and an influential set is generated based on the behaviour of other nodes due to the influence of the parent nodes. The major disadvantage of this mechanism is the potential creation of vulnerable nodes which spread the information without knowing the adverse effect on the individual. For example, advertising the junk food attractively may have impact on the obese person. The issue with this approach is influencing the entire population using vulnerable nodes. The proposed model tries to influence the targeted audience by maximizing the non vulnerable nodes in the graph. Since the interaction is associated with the behavioural patterns of the individuals, the model uses the genetic algorithm termed as Enhanced Shuffled Frog-Leaping. It searches the local space by encrypting the cumulative responses from other nodes and it updates the fitness function based on the utility. It is evident from the obtained experimental results that the proposed Enhanced Shuffled Frog Leaping Approach for Influence Maximization (EFSLIM) in social network showed the influence spread and statistical tests as an effective and advanced model for overcoming the influence maximization problems. The proposed model showed better performance and reached 1400 of spreading size for 100th node but the existing DFLA obtained 800 of spreading size.
Keywords:	Influence Maximization, Seed Nodes, Shuffled Frog Leaping Algorithm, Social networks, Spreading Size
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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Title:	BE-MCSDMA : BI-LEVEL ENCRYPTED MULTI CLOUD SECURE DATA MANAGEMENT ARCHITECTURE
Author:	DAMISETTI VEERABHADRARAO1 , G APPARAO ,ANURADHA S
Abstract:	During this information age, every sector is generating massive data on daily basis and this data is to be stored so that the storage entities possess vital characteristics such as availability, integrity, authenticity, and offering secure, confidential, simple, and fast retrieval. Right from the file systems, the storage architecture evolved to today’s hybrid cloud and maybe tomorrow’s quantum cloud to meet the prevailing customized requirements. Cloud computing is a legendary technology that is necessary for all business segments in terms of efficient data storage needs. It is more beneficial to those organizations which cannot afford much on the computing infrastructure to avail of various major cloud services like IaaS, PaaS, SaaS, and other supplementary ones. Cloud technology has drastically modulated the service and industrial sectors to an extent that cyber attackers are relying much more to capitalize on even miniature leaks despite the exhaustive security measures. The technology took its shape in offering various types like Public, Private, Hybrid, Multi and Multi-Hybrid clouds. Every technology is a threat prone not leaving cloud-related as an exception. Due to Covid 19 pandemic, the Health segment is revolutionized in which enormous data is generated worldwide. Not only in the health domain but also in various sectors the data is generated at a rapid speed. Whichever the industry, the data is to be immensely protected. In this research fragment, we are intended to design novel security architecture for a multi-cloud environment applied to Electronic Health Records (EHR) and analyze it’s working.
Keywords:	Multi Cloud Security, Cloud Security In Health Sector, Multi Cloud Computing, E-Health Cloud Security
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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Title:	ENHANCING BRAND AWARENESS THROUGH DIGITAL COMMUNICATION STRATEGIES
Author:	YOSSY KAMADI, MUHAMMAD ARAS, NOVI ANDRIANI, HASHIFAH DZATI BAYANI
Abstract:	The era of globalization change the way people interact from traditional to modern ways. This change also occurs on interpersonal relationships and also the way they interact in business. Nowadays, people and companies are using technology almost in every aspect of their life. Marketing in this day should be supported by digital marketing strategy to be marketed optimally. Digital marketing strategy has an important role to make people aware and improve the existence of a company. This study aims to analyze digital communication strategies in enchancing brand awareness in logistic company. Logistic company have to intergrate their digital marketing communication strategies between buyer and seller. The research method used in this study is a qualitative descriptive method. The type of data used in this study is qualitative data. Sources of data obtained through interview techniques. The results of the study concluded that in carrying out digital marketing strategic communications the company must have the right strategy so that all predetermined plans can be achieved. Every company must have a strategy to make his business known to consumers. A good strategy will provide benefits for the company as the realization of the company's goals. Brand awareness has an important effect on consumer decision-making by influencing the brands that are considered, and also the influence of the brands selected from consideration. Brand Awareness can make consumers think about using or buying it.
Keywords:	Digital Communication, Brand Awareness, Digital Marketing, Strategy, Social Media
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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Title:	EXPERIMENTAL INVESTIGATIONS TO FACE RECOGNITION USING OPENCV
Author:	BANGARU LAKSHMI MAHANTHI, Dr. ANURADHA SESETTI
Abstract:	There are various types of biometric identifications including DNA, fingerprints, signature recognition, hand geometry, palm print, for this type of recognition; some action has to be done by the user like placing a finger on the machine to detect. While, Face Recognition does not require any user actions .It plays a vital role in fields such as identifying the retail crimes, finding missing person, to help the blind, forensic, to diagnose s, unlocks smart phones, secure transactions, validates identity, control access to sensitive areas etc. The challenge that can be encountered in face recognition is to detect the face from single image that is stored in the database. Face detection is a challenging task as the faces are not rigid and they will be changing in size, shape, colour, it become more challenging task when given image is not clear and not containing a proper lightning, not facing camera etc. The digital image can be obtained from a video frame or from live images detected by the camera/webcam. It involves two stages. First we have to detect the face. For this process, a photo is searched to find face, after finding the face in the image, which is processed to crop and extract the person’s face in square box, the second phase is Face Recognition, where the face detected by above process is compared with the images in the dataset, to decide who that person is. In this work, we are using the HOG algorithm. A Face can be detected by cropping the face and removing the noise using the HAAR cascade classifier. Later the features of the face can be extracted by using the HOG extractor. Then we will run an SVM model for face recognition. The result is an evident for accuracy and efficiency.
Keywords:	OpenCV, HAAR, HOG, Monitoring
Source:	Journal of Theoretical and Applied Information Technology 15^th December 2022 -- Vol. 100. No. 23-- 2022
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